- Referenced in 364 articles
- BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian...
- Referenced in 189 articles
- programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum...
- Referenced in 44 articles
- package BayesLogit: PolyaGamma Sampling. Bayesian inference for logistic models using Pólya-Gamma latent variables...
- Referenced in 74 articles
- software package for performing Bayesian inference Using Gibbs Sampling. The user specifies a statistical model...
- Referenced in 59 articles
- accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively...
- Referenced in 25 articles
- evidential reasoning in large Bayesian networks Stochastic sampling algorithms, while an attractive alternative to exact ... algorithms in very large Bayesian network models, have been observed to perform poorly in evidential ... importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks ... self-importance sampling. We used in our tests three large real Bayesian network models available...
- Referenced in 91 articles
- package boa: Bayesian Output Analysis Program (BOA) for MCMC. A menu-driven program and library ... graphical analysis of Markov chain Monte Carlo sampling output...
- Referenced in 38 articles
- MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. We present ... multimodal nested sampling algorithm, called MultiNest. This Bayesian inference tool calculates the evidence, with...
- Referenced in 64 articles
- specification of the probability model. In the Bayesian context, this is accomplished by placing ... function spaces are highly complex and hence sampling methods play a key role. This paper ... programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently ... process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual...
- Referenced in 50 articles
- from R to the JAGS library for Bayesian data analysis. JAGS uses Markov Chain Monte ... MCMC) to generate a sequence of dependent samples from the posterior distribution of the parameters...
- Referenced in 23 articles
- OpenBUGS software for Bayesian analysis using MCMC sampling. Runs natively and stably...
- Referenced in 13 articles
- across multiple trees (such as a Bayesian posterior sample); conducting an analysis called stochastic character...
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- Variable Selection and Model Averaging using Bayesian Adaptive Sampling. Package for Bayesian Variable Selection ... generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions...
- Referenced in 7 articles
- EasyABC: Efficient Approximate Bayesian Computation Sampling Schemes. Enables launching a series of simulations...
- Referenced in 10 articles
- scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness...
- Referenced in 35 articles
- PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov ... large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output...
- Referenced in 49 articles
- Package. This package contains functions to perform Bayesian inference using posterior simulation for a number ... statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization...
- Referenced in 6 articles
- dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences. We present dynesty ... estimate Bayesian posteriors and evidences (marginal likelihoods) using Dynamic Nested Sampling. By adaptively allocating samples...
- Referenced in 6 articles
- Adaptive Monte Carlo Markov Chain Sampler. Enables sampling from arbitrary distributions if the log density ... situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola...
- Referenced in 3 articles
- sampling with PolyChord. Nested sampling is a numerical method for Bayesian computation which simultaneously calculates ... posterior samples and an estimate of the Bayesian evidence for a given likelihood and prior ... Markov chain Monte Carlo (MCMC)-based sampling for multi-modal or degenerate posterior distributions. dyPolyChord...